205 lines
6.3 KiB
Python
205 lines
6.3 KiB
Python
# -*- encoding:utf-8 -*-
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'''
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@Author : dingjiawen
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@Date : 2023/11/10 14:56
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@Usage :
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@Desc : 训练LSTM
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'''
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import os
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import time
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import random
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import matplotlib.pyplot as plt
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from torch.utils.data import DataLoader
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from RUL.otherIdea.dctLSTM.modelForEasy import PredictModel
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from RUL.otherIdea.dctLSTM.loadData import getDataset
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from RUL.otherIdea.dctLSTM.test import test
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from RUL.baseModel.CommonFunction import IsStopTraining
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import RUL.baseModel.utils.utils as utils
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import math
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'''
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超参数设置:
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'''
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hidden_num = 40 # LSTM细胞个数
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feature = 10 # 一个点的维度
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batch_size = 32
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EPOCH = 1000
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predict_num = 200 # 预测个数
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seed = 250
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is_norm = False
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is_single = True
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model_name = "dctLSTM"
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base_save = r"parameters/{0}_hidden{1}_feature{2}_predict{3}".format(model_name, hidden_num, feature,
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predict_num)
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save_fig_name = 'fig/seed{0}_hidden{1}_feature{2}_predict{3}'.format(seed, hidden_num, feature, predict_num)
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if not os.path.exists("parameters"):
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os.makedirs("parameters")
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if not os.path.exists("fig"):
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os.makedirs("fig")
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def get_dataset():
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'''得到数据集'''
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train_dataset, val_dataset = getDataset(
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hidden_num=hidden_num, feature=feature, predict_num=predict_num, is_single=is_single, is_norm=is_norm)
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'''DataLoader'''
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train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=False)
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val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=False, drop_last=False)
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return train_loader, val_loader
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def train(device, lr, lr_patience, early_stop_patience, epochs):
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'''预测模型'''
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global best_save_path
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model = PredictModel(input_dim=feature)
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'''得到数据集'''
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train_loader, val_loader = get_dataset()
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criterion = nn.MSELoss().to(device)
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optimizer_model = torch.optim.SGD(model.parameters(), lr=lr)
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scheduler_model = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_model, mode="min", factor=0.5,
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patience=lr_patience)
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def zero_grad_all():
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optimizer_model.zero_grad()
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train_loss_list = []
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val_loss_list = []
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best_save_path = None
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for epoch in range(epochs):
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epoch_start_time = time.time()
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train_loss = 0.0
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val_loss = 0.0
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model.train()
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for (train_batch_idx, (train_data, train_label)) in enumerate(train_loader):
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train_data, train_label = train_data.to(device), train_label.to(device)
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zero_grad_all()
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predict_data = torch.squeeze(model(train_data))
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# MSE损失
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loss = criterion(predict_data, train_label)
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loss.backward()
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optimizer_model.step()
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zero_grad_all()
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train_loss += loss.item()
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model.eval()
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with torch.no_grad():
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for val_batch_idx, (val_data, val_label) in enumerate(val_loader):
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val_data, val_label = val_data.to(device), val_label.to(device)
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val_predict_data = torch.squeeze(model(val_data))
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loss = criterion(val_predict_data, val_label)
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val_loss += loss.item()
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scheduler_model.step(val_loss)
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train_loss = train_loss / len(train_loader)
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val_loss = val_loss / len(val_loader)
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print(
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"[{:03d}/{:03d}] {:2.2f} sec(s) train_loss: {:3.9f} | val_loss: {:3.9f} | Learning rate : {:3.6f}".format(
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epoch + 1, epochs, time.time() - epoch_start_time,
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train_loss,
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val_loss,
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optimizer_model.state_dict()['param_groups'][0]['lr']))
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# 保存在验证集上loss最小的模型
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# if val_loss_list.__len__() > 0 and (val_loss / val_dataset.__len__()) < min(val_loss_list):
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# 如果精度大于最高精度，则保存
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if len(val_loss_list) == 0 or val_loss < min(val_loss_list):
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print("保存模型最佳模型成功")
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# 保存模型参数
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if best_save_path != None:
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utils.delete_file(best_save_path)
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best_save_path = base_save + "_epoch" + str(epoch) + \
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"_trainLoss" + str(train_loss) + \
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"_valLoss" + str(val_loss) + ".pkl"
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torch.save(model.state_dict(),
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best_save_path)
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train_loss_list.append(train_loss)
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val_loss_list.append(val_loss)
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if IsStopTraining(history_loss=val_loss_list, patience=early_stop_patience):
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break
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'''保存的模型参数的路径'''
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from matplotlib import rcParams
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config = {
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"font.family": 'Times New Roman', # 设置字体类型
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"axes.unicode_minus": False, # 解决负号无法显示的问题
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"axes.labelsize": 13
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}
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rcParams.update(config)
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pic1 = plt.figure(figsize=(12, 6), dpi=200)
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plt.plot(np.arange(1, len(train_loss_list) + 1), train_loss_list, 'b', label='Training Loss')
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plt.plot(np.arange(1, len(train_loss_list) + 1), val_loss_list, 'r', label='Validation Loss')
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# plt.ylim(0, 0.08) # 设置y轴范围
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plt.title('Training & Validation loss')
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plt.xlabel('epoch')
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plt.ylabel('loss')
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plt.legend(loc='upper right')
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plt.grid(alpha=0.4)
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# 获取当前时间戳
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timestamp = int(time.time())
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# 将时间戳转换为字符串
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timestamp_str = str(timestamp)
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plt.savefig(timestamp_str, dpi=200)
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plt.show()
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return best_save_path
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if __name__ == '__main__':
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begin = time.time()
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if torch.cuda.is_available():
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device = torch.device("cuda:0")
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else:
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device = torch.device("cpu")
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torch.manual_seed(seed)
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random.seed(seed)
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np.random.seed(seed)
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'''训练'''
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save_path = train(device, lr=0.01, lr_patience=10, early_stop_patience=20, epochs=1000)
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end = time.time()
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'''测试'''
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# test1(5, src_condition, tar_condition, G_params_path, LC_params_path)
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test(hidden_num, feature, predict_num=predict_num,
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batch_size=batch_size, save_path=save_path,
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is_single=is_single, is_norm=is_norm,save_fig_name=save_fig_name)
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print("训练耗时:{:3.2f}s".format(end - begin))
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